SoroushVahidi/scidocs huggingface dataset:
Waste Management as an IoT-Enabled Service in Smart Cities
(2015)

Intelligent Transportation Systems (ITS) enable new services within Smart Cities. Efficient Waste Collection is considered a fundamental service for Smart Cities. Internet of Things (IoT) can be applied both in ITS and Smart cities forming an advanced platform for novel applications. Surveillance systems can be used as an assistive technology for high Quality of Service (QoS) in waste collection. Specifically, IoT components: (i) RFIDs, (ii) sensors, (iii) cameras, and (iv) actuators are incorporated into ITS and surveillance systems for efficient waste collection. In this paper we propose an advanced Decision Support System (DSS) for efficient waste collection in Smart Cities. The system incorporates a model for data sharing between truck drivers on real time in order to perform waste collection and dynamic route optimization. The system handles the case of ineffective waste collection in inaccessible areas within the Smart City. Surveillance cameras are incorporated for capturing the problematic areas and provide evidence to the authorities. The waste collection system aims to provide high quality of service to the citizens of a Smart City.
Internet of Things Approach for Automation of the Complex Industrial Systems
(2017)

This paper presents the analysis of the Internet of Things (IoT) approach and its application for the development of embedded monitoring and automatic control systems (EMACS) for technological objects and processes that are included in complex industrial systems. The functional structure and main components of EMACS based on IoT approach are given. The examples of IoT applications in design of specialized EMACS for such complex technical objects as gas turbine engines and floating docks are presented. Considerable attention is given to particular qualities of the functional structures, software and hardware implementation as well as multi-level human-machine interfaces of the EMACS for main process parameters. The developed EMACS provide: (a) high precision control of real-time operating processes of gas turbine engines and floating docks, (b) monitoring and automatic control of current technological parameters with high quality indicators, that leads to significant increasing of energy and economic efficiency of both industrial objects.
Human Motion Reconstruction from Action Video Data Using a 3-Layer-LSTM
(2017)

Data driven motion generation is a challenging problem. Typically the model is trained using motion capture (MOCAP) data, which is limited in scope and diversity. Solving this problem using motion data extracted from Youtube videos would enable motion generation pipelines to feed from a wider range of action data, allowing for greater variability in the actions generated. Human action is expressed through a sequence of bodily movements. Hence, in this work we explore three stages for the motion synthesis pipeline: first by extracting human pose from video data with Part Affinity Fields method by [1], and then by training a 3-Layer Recurrent Neural Network (RNN) that learns to generate human-like motion for a given class from annotated video data, and finally we explore a simple synthesis of motion from multiple classes. Ultimately, this approach could enable robust generation of a wide range of motions, capturing the the subtle and fine variations of human movement expressed in videos that populate the Internet to artificially produce life like motion.
EVE: explainable vector based embedding technique using Wikipedia
(2018)

We present an unsupervised explainable vector embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a concept using human-readable labels, thereby it is readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed model, we consider its usefulness in three fundamental tasks: 1) intruder detection—to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster—to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first—to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.
Utility max-min fair resource allocation for communication networks with multipath routing
(2009)

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max-min fairness is achieved, which is essential for balancing QoS (Quality of Service) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max-min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.
3D High-Efficiency Video Coding for Multi-View Video and Depth Data
(2013)

This paper describes an extension of the high efficiency video coding (HEVC) standard for coding of multi-view video and depth data. In addition to the known concept of disparity-compensated prediction, inter-view motion parameter, and inter-view residual prediction for coding of the dependent video views are developed and integrated. Furthermore, for depth coding, new intra coding modes, a modified motion compensation and motion vector coding as well as the concept of motion parameter inheritance are part of the HEVC extension. A novel encoder control uses view synthesis optimization, which guarantees that high quality intermediate views can be generated based on the decoded data. The bitstream format supports the extraction of partial bitstreams, so that conventional 2D video, stereo video, and the full multi-view video plus depth format can be decoded from a single bitstream. Objective and subjective results are presented, demonstrating that the proposed approach provides 50% bit rate savings in comparison with HEVC simulcast and 20% in comparison with a straightforward multi-view extension of HEVC without the newly developed coding tools.
Fuzzy logic controller for an inverted pendulum system using quantum genetic optimization
(2011)

In this paper, we propose a new generalized design methodology of intelligent robust fuzzy control systems based on quantum genetic algorithm (QGA) called quantum fuzzy model that enhance robustness of fuzzy logic controllers. The QGA is adopted because of their capabilities of directed random search for global optimization to find the parameters of the shape and width of membership functions and rule set of the FLC to obtain the optimal fuzzy controller simultaneously. We test the optimal FLC obtained by the quantum computing applied on the control of dynamic balance and motion of cart-pole balancing system. We compare the proposed technique with existing mamdani fuzzy logic controller which is designed through conventional genetic algorithm. Simulation results reveal that QGA performs better than conventional GA in terms of running speed and optimizing capability.
Sparse Recovery of Hyperspectral Signal from Natural RGB Images
(2016)

Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input.
Supervised Learning Based Algorithm Selection for Deep Neural Networks
(2017)

Many recent deep learning platforms rely on thirdparty libraries (such as cuBLAS) to utilize the computing power of modern hardware accelerators (such as GPUs). However, we observe that they may achieve suboptimal performance because the library functions are not used appropriately. In this paper, we target at optimizing the operations of multiplying a matrix with the transpose of another matrix (referred to as NT operation hereafter), which contribute half of the training time of fully connected deep neural networks. Rather than directly calling the library function, we propose a supervised learning based algorithm selection approach named MTNN, which uses a gradient boosted decision tree to select one from two alternative NT implementations intelligently: (1) calling the cuBLAS library function; (2) calling our proposed algorithm TNN that uses an efficient out-of-place matrix transpose. We evaluate the performance of MTNN on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve 96% of prediction accuracy with very low computational overhead, which results in an average of 54% performance improvement on a range of NT operations. To further evaluate the impact of MTNN on the training process of deep neural networks, we have integrated MTNN into a popular deep learning platform Caffe. Our experimental results show that the revised Caffe can outperform the original one by an average of 28%. Both MTNN and the revised Caffe are open-source.
Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey
(2014)

This paper represents energy efficient routing protocols in WSN. It is a collection of sensor nodes with a set of limited Processor and limited memory unit embedded in it. Reliable routing of packets from the sensor node to its base station is the most important task for the networks. The routing protocols applied for the other networks cannot be used here due to its battery powered nodes This paper gives an overview of the different routing strategies used in wireless sensor networks and gives a brief working model of energy efficient routing protocols in WSN. It also shows the comparison of these different routing protocols based on metrics such as mobility support, stability, issues and latency.
All-Digital Background Calibration of a Successive Approximation ADC Using the “Split ADC” Architecture
(2011)

The “split ADC” architecture enables fully digital calibration and correction of nonlinearity errors due to capacitor mismatch in a successive approximation (SAR) ADC. The die area of a single ADC design is split into two independent halves, each converting the same input signal. Total area and power is unchanged, resulting in minimal increase in analog complexity. For each conversion, the half-sized ADCs generate two independent outputs which are digitally corrected using estimates of capacitor mismatch errors for each ADC. The ADC outputs are averaged to produce the ADC output code. The difference of the two outputs is used in a background calibration algorithm which estimates the error in the correction parameters. Any nonzero difference drives an LMS feedback loop toward zero difference which can only occur when the average error in each correction parameter is zero. A novel segmentation and shuffling scheme in the SAR capacitive DAC enables background calibration for a wide range of input signals including dc. Simulation of a 16 bit 1 Msps SAR ADC in 180 nm CMOS shows calibration convergence within 200 000 samples.
A Survey of Face Recognition Techniques
(2009)

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.
Algebraic feature extraction of image for recognition
(1991)

The extraction of image features is one of the fundamental tasks in image recognition . Up until now, there have been several kinds of features to be used for the purpose of image recognition as follows: (1) visual features ; (2) statistical features of pixel ; (3) transform coefficient features . In addition, there is another kind of feature which the author believes is very useful, i.e . (4) algebraic features which represent intrinsic attributions of an image . Singular Values (SV) of image are this kind of feature . In this paper, we prove that SV feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise . These properties are very useful for the description and recognition of images . As an example, SV feature vector is used for the problem of recognizing human facial images . In this paper, using SV feature vector samples of facial images, a normal pattern Bayes classification model based on Sammon s optimal descriminant plane is constructed . The experimental result shows that SV feature vector has good performance of class separation . Image recognition Algebraic feature extraction Singular value feature Facial image recognition Discrinlinant vector Dimensionality reduction
Parameterisation of a stochastic model for human face identification
(1994)

Stochastic modelling of non-stationary vector timeseries based on HMMs has been very successful for speech applications [5]. Recently it has been applied to a range of image recognition problems [7, 9]. Previously reported work [6] has investigated the use of HMMs to model human faces for identi cation purposes. Faces can be intuitively divided into regions such as the mouth, eyes, nose, etc., and these regions can be associated with the states of an HMM. The identi cation performance of a top-bottom HMM compares favourably with some of the well-known algorithms, for example eigenfaces as detailed in [8]. However, the HMM parameterisation in the work presented so far was arrived at by subjective intuition. This paper presents experimental results which show how identi cation rates vary with HMM parameters, and which indicate the most sensible choice of parameters. The paper is organised as follows: section 2 gives an overview of the HMM-based approach; section 3 details the training and recognition processes; section 4 describes the experimental setup; section 5 presents the identi cation results; section 6 concludes the paper.
A comparative study of texture measures with classification based on featured distributions
(1996)

-This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented Texture analysis Classification Feature distribution Kullback discriminant Performance evaluation Brodatz textures l. I N T R O D U C T I O N Texture is an important characteristic for the analysis of many types of images. A wide variety of measures for discriminating textures have been proposed, c1'2~ Comparative studies to evaluate the performance of some texture measures have been carried out by Weszka et al., ~3~ Du Bufet al. ~4~ and Ohanian and Dubes, ~s) for
Video-Based Face Recognition Using Probabilistic Appearance Manifolds
(2003)

This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihoo d that the input image comes from a particular pose manifold and the transition probability to this pose manifold from th e previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes.
Theory application by school-based occupational therapists.
(1996)

OBJECTIVES
Recent literature indicates that there is an inconsistent use of theory to guide clinical actions by occupational therapists, including those working in pediatrics. The purpose of this study was to describe school-based therapists' theory application by collecting information about what frames of reference they used and why.


METHOD
Of the 72 school-based therapists in the mid-Atlantic states who agreed to respond to a questionnaire, 51 (70.8%) returned the questionnaire. Information about demographics, what frames of reference were used, and why they were used was obtained from the questionnaire.


RESULTS
Respondents reported using a multitheoretical approach, with sensory integration theory and neurodevelopmental theory being the predominate frames of reference applied but not the only ones used. The frames of reference were used on the basis of several factors, including the children's needs and the respondent's education.


CONCLUSION
Formal and continuing education seems to have a great effect on school-based occupational therapists as they develop their personal conceptual frameworks.
Geotagging Named Entities in News and Online Documents
(2016)

News sources generate constant streams of text with many references to real world entities; understanding the content from such sources often requires effectively detecting the geographic foci of the entities. We study the problem of associating geography to named entities in online documents. More specifically, given a named entity and a page (or a set of pages) where the entity is mentioned, the problem being studied is how the geographic focus of the name can be resolved at a location granularity (e.g. city or country), assuming that the name has a geographic focus. We further study dispersion, and show that the dispersion of a name can be estimated with a good accuracy, allowing a geo-centre to be detected at an exact dispersion level. Two key features of our approach are: (i) minimal assumption is made on the structure of the mentions hence the approach can be applied to a diverse and heterogeneous set of web pages, and (ii) the approach is unsupervised, leveraging shallow English linguistic features and the large volume of location data in public domain.
 We evaluate our methods under different task settings and with different categories of named entities. Our evaluation reveals that the geo-centre of a name can be estimated with a good accuracy based on some simple statistics of the mentions, and that the accuracy of the estimation varies with the categories of the names.
Increased human intestinal barrier permeability plasma biomarkers zonulin and FABP2 correlated with plasma LPS and altered gut microbiome in anxiety or depression.
(2018)

We read with interest the recent work by Uhde et al, which demonstrated that physically asymptomatic non-coeliac gluten/ wheat sensitivity involves compromised intestinal epithelium barrier dysfunction in conjunction with systemic immune activation events. We also read with interest the recent work by Marchesi et al, which comprehensively reviewed the role of microbiota in physical disorders of the gut and extra-gut organs. But common to these Gut papers was the lack of accounting for anxiety and depression, comorbidities often experienced in gastroenterology clinics. Patients who are otherwise physically asymptomatic often do not explicitly divulge these mental disorders, or the disorders are unintentionally overlooked, yet they report ‘diminished quality of life’. In response to this gap, we explored roles of dysbiosis and gut barrier integrity in individuals physically asymptomatic for gastrointestinal distress, yet nonetheless experiencing mental distress. We hypothesised that anxiety and depressive disorders are linked to human gut dysbiosis with microbiota that secrete lipopolysaccharide (LPS) endotoxin into plasma, which in conjunction with compromised gut barrier integrity has systemic manifestations including the brain. We further hypothesised that this correlates with altered intestinal epithelium paracellular integrity molecules discharged into blood as biomarkers, namely zonulin and intestinal fatty acidbinding protein-2 (FABP2). Zonulin is the master regulator of endothelial and epithelium tight junctions, modulating both gut and blood–brain barriers. We analysed stool faecal microbiota and plasma in 50 subjects who self-reported to be asymptomatic for gastrointestinal physical disorders; 22 volunteers met Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for a depressive disorder or an anxiety disorder (DEP/ ANX), and 28 more were control reference subjects (NODEPANX). Subjects refrained from probiotics and antibiotics during 30 days prior to providing samples (see online supplementary methods). LEfSe analyses (online supplementary figures S1 and S2) revealed gut dysbiosis in the DEP/ANX subjects, in contrast to microbiome of control subjects. PICRUSt analyses (figure 1) predicted KEGG orthologue gene differences consistent with a pathophysiological gut metagenomic signature in DEP/ANX versus NODEPANX microbiome. Notable in figure 1 were over-represented LPS biosynthesis genes in the gut microbiome of PostScript Gut Online First, published on August 16, 2017 as 10.1136/gutjnl-2017-314759